BIC                   package:nlme                   R Documentation

_B_a_y_e_s_i_a_n _I_n_f_o_r_m_a_t_i_o_n _C_r_i_t_e_r_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This generic function calculates the Bayesian information
     criterion, also known as Schwarz's Bayesian criterion (SBC), for
     one or several fitted model objects for which a log-likelihood
     value can be obtained, according to the formula -2*log-likelihood
     + npar*log(nobs), where npar  represents the number of parameters
     and nobs the number of observations in the fitted model.

_U_s_a_g_e:

     BIC(object, ...)

_A_r_g_u_m_e_n_t_s:

  object: a fitted model object, for which there exists a 'logLik'
          method to extract the corresponding log-likelihood, or an
          object inheriting from class 'logLik'.

     ...: optional fitted model objects.

_V_a_l_u_e:

     if just one object is provided, returns a numeric value with the
     corresponding BIC; if more than one object are provided, returns a
     'data.frame' with rows corresponding to the objects and columns
     representing the number of parameters in the model ('df') and the
     BIC.

_A_u_t_h_o_r(_s):

     Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates
     bates@stat.wisc.edu

_R_e_f_e_r_e_n_c_e_s:

     Schwarz, G. (1978) "Estimating the Dimension of a Model", Annals
     of Statistics, 6, 461-464.

_S_e_e _A_l_s_o:

     'logLik', 'AIC', 'BIC.logLik'

_E_x_a_m_p_l_e_s:

     data(Orthodont)
     fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
     BIC(fm1)
     fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age
     BIC(fm1, fm2)

